Boosting with Multiple SourcesDownload PDF

21 May 2021, 20:50 (modified: 25 Jan 2022, 11:49)NeurIPS 2021 PosterReaders: Everyone
Keywords: boosting, multiple sources, generalization bounds, federated learning
TL;DR: Novel multi-source boosting algorithm with domain-weighted combination of weak learners.
Abstract: We study the problem of learning accurate ensemble predictors, in particular boosting, in the presence of multiple source domains. We show that the standard convex combination ensembles in general cannot succeed in this scenario and adopt instead a domain-weighted combination. We introduce and analyze a new boosting algorithm, MULTIBOOST, for this scenario and show that it benefits from favorable theoretical guarantees. We also report the results of several experiments with our algorithm demonstrating that it outperforms natural baselines on multi-source text-based, image-based and tabular data. We further present an extension of our algorithm to the federated learning scenario and report favorable experimental results for that setting as well. Additionally, we describe in detail an extension of our algorithm to the multi-class setting, MCMULTIBOOST, for which we also report experimental results.
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